{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ec9cceb8-45c3-4945-8b8f-fb281bd9fb22",
   "metadata": {},
   "source": [
    "⽹络中的⽹络（NIN）"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae26b99a-b476-48b3-8356-5e3d45a5bfeb",
   "metadata": {},
   "source": [
    "AlexNet和VGG对LeNet的改进主要在于如何对这两个模块加宽（增加通道数）和加深。\n",
    "NiN网络提供了另外的思路:串联多个由卷积层和全连接层构成的小网络来构建深层的网络"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8b0e0e9-26c8-4c0e-98b8-022e98bb1b22",
   "metadata": {},
   "source": [
    "卷积层的输⼊和输出通常是四维数组（样本，通道，⾼，宽），⽽全连接层的输⼊和输出则\n",
    "通常是⼆维数组（样本，特征）。如果想在全连接层后再接上卷积层，则需要将全连接层的输出变换为四维。"
   ]
  },
  {
   "attachments": {
    "155f464c-8a53-40ae-9cde-d37f9c809325.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "24046475-3010-4412-ac93-7308b0f292c8",
   "metadata": {},
   "source": [
    "![image.png](attachment:155f464c-8a53-40ae-9cde-d37f9c809325.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f39a9ec-e473-4cf2-8330-36038bf4b4b9",
   "metadata": {},
   "source": [
    "NiN块是NiN中的基础块。它由一个卷积层充当全连接层,其中第一个全连接层的超参数可以自己设置,一般第二第三层超参数是固定的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d9c18979-f878-4062-8a77-da9cbc0d97e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import torch\n",
    "from torch import nn,optim\n",
    "\n",
    "import sys\n",
    "sys.path.append(\"..\")\n",
    "import d2lzh_pytorch as d2l\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "\n",
    "#使用1×1的卷积层充当全连接层\n",
    "def nin_block(in_channels, out_channels, kernel_size, stride, padding):\n",
    "    blk = nn.Sequential(\n",
    "        nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding),\n",
    "        nn.ReLU(),\n",
    "        nn.Conv2d(out_channels, out_channels, kernel_size=1),\n",
    "        nn.ReLU(),\n",
    "        nn.Conv2d(out_channels, out_channels, kernel_size=1),\n",
    "        nn.ReLU()\n",
    "    )\n",
    "    return blk"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21f81918-6133-4f4d-86bf-baa2d6c1db9a",
   "metadata": {},
   "source": [
    "1.2NiN模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "176379c0-16e1-4c5f-8c9c-be03bb1a6a5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "class GlobalAvgPool2d(nn.Module):\n",
    "    # 全局平均池化层可通过将池化窗⼝形状设置成输⼊的⾼和宽实现\n",
    "    def __init__(self):\n",
    "        super(GlobalAvgPool2d, self).__init__()\n",
    "    def forward(self, x):\n",
    "        return F.avg_pool2d(x, kernel_size=x.size()[2:])\n",
    "#使用输出通道等于标签数的方法,来进行分类\n",
    "net = nn.Sequential(\n",
    "    nin_block(1, 96, kernel_size=11, stride=4, padding=0),\n",
    "    nn.MaxPool2d(kernel_size=3, stride=2),\n",
    "    nin_block(96, 256, kernel_size=5, stride=1, padding=2),\n",
    "    nn.MaxPool2d(kernel_size=3, stride=2),\n",
    "    nin_block(256, 384, kernel_size=3, stride=1, padding=1),\n",
    "    nn.MaxPool2d(kernel_size=3, stride=2),\n",
    "    nn.Dropout(0.5),\n",
    "    nin_block(384, 10, kernel_size=3, stride=1, padding=1),\n",
    "    GlobalAvgPool2d(),\n",
    "    d2l.FlattenLayer()\n",
    "    \n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0abf6d0d-a4fa-4433-ab49-448232b89cf5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 output shape:  torch.Size([1, 96, 54, 54])\n",
      "1 output shape:  torch.Size([1, 96, 26, 26])\n",
      "2 output shape:  torch.Size([1, 256, 26, 26])\n",
      "3 output shape:  torch.Size([1, 256, 12, 12])\n",
      "4 output shape:  torch.Size([1, 384, 12, 12])\n",
      "5 output shape:  torch.Size([1, 384, 5, 5])\n",
      "6 output shape:  torch.Size([1, 384, 5, 5])\n",
      "7 output shape:  torch.Size([1, 10, 5, 5])\n",
      "8 output shape:  torch.Size([1, 10, 1, 1])\n",
      "9 output shape:  torch.Size([1, 10])\n"
     ]
    }
   ],
   "source": [
    "X = torch.rand(1, 1, 224, 224)\n",
    "for name, blk in net.named_children():\n",
    "    X = blk(X)\n",
    "    print(name, 'output shape: ', X.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7febcda-4f78-47e0-a294-fce86bc98e51",
   "metadata": {},
   "source": [
    "1.3获取数据和训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e02852a1-f326-4bff-9b5d-cee21dc6bfa5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training on cuda\n",
      "epoch 1, loss 1.0321, train acc 0.612, test acc 0.789, time 80.0 sec\n",
      "epoch 2, loss 0.5211, train acc 0.807, test acc 0.817, time 79.1 sec\n",
      "epoch 3, loss 0.4501, train acc 0.833, test acc 0.828, time 78.6 sec\n",
      "epoch 4, loss 0.4063, train acc 0.848, test acc 0.851, time 79.4 sec\n",
      "epoch 5, loss 0.3792, train acc 0.858, test acc 0.857, time 81.7 sec\n"
     ]
    }
   ],
   "source": [
    "batch_size = 128\n",
    "train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size, resize=224)\n",
    "\n",
    "lr, num_epochs = 0.002, 5\n",
    "optimizer = torch.optim.Adam(net.parameters(), lr=lr)\n",
    "d2l.train_ch5(net, train_iter, test_iter, batch_size, optimizer, device, num_epochs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cde47cb4-4ec9-41ae-9797-64669f074608",
   "metadata": {},
   "source": [
    "总结:\n",
    "NiN去除了容易造成过拟合的全连接输出层,而是将其替换成输出通道等于标签类别数的NiN块和全局平均池化层"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0d2dd37-1701-4665-850c-f86ff2f07365",
   "metadata": {},
   "source": [
    "🔹 2. NiN 如何应用到 PointNet++\n",
    "PointNet++ 主要由 局部特征提取（MLP 计算点特征）+ 层级特征聚合（Set Abstraction）+ 分类/分割模块 组成。可以在以下地方引入 NiN：\n",
    "\n",
    "✅ (1) 用 NiN 替换全连接层\n",
    "PointNet++ 传统的分类/分割模块使用全连接层（FC），可以替换为 NiN：\n",
    "\n",
    "在 MLP 层中用 1×1 卷积代替 FC，减少参数量，提高计算效率。\n",
    "使用 NiN 结构（多个 1×1 卷积叠加 ReLU）提升特征表达能力。\n",
    "\n",
    "✅ (2) 在特征聚合阶段使用 NiN\n",
    "PointNet++ 需要在 Set Abstraction 阶段进行局部特征提取并聚合，可以在 Grouping & Pooling 之后加上 NiN 1×1 卷积 进行特征变换。\n",
    "\n",
    "✅ (3) 用全局平均池化代替 FC\n",
    "PointNet++ 在最终输出分类或分割结果时，可以用 GlobalAvgPool1D 取代 MLP+FC，减少参数量，缓解过拟合。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb08edfa-4a9d-4dec-9f97-01db83e572e0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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